Skip to Main content Skip to Navigation
Conference papers

Deep Sets for Generalization in RL

Tristan Karch 1 Cédric Colas 1 Laetitia Teodorescu 1 Clément Moulin-Frier 1 Pierre-Yves Oudeyer 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent. This is done using a combination of object-wise permutation invariant networks inspired from Deep Sets and gated-attention mechanisms. In a 2D procedurally-generated world where agents targeting goals in natural language navigate and interact with objects, we show that these architectures demonstrate strong generalization capacities to out-of-distribution goals. We study the generalization to varying numbers of objects at test time and further extend the object-centered architectures to goals involving relational reasoning.
Document type :
Conference papers
Complete list of metadatas
https://hal.inria.fr/hal-03120669
Contributor : Tristan Karch <>
Submitted on : Tuesday, January 26, 2021 - 12:13:36 PM
Last modification on : Friday, March 5, 2021 - 10:50:06 AM

File

Vignette du document
2003.09443.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03120669, version 1
  • ARXIV : 2003.09443

Collections

IP_PARIS | INRIA | ENSTA

Citation

Tristan Karch, Cédric Colas, Laetitia Teodorescu, Clément Moulin-Frier, Pierre-Yves Oudeyer. Deep Sets for Generalization in RL. Beyond Tabula Rasa in Reinforcement Learning: agents that remember adapt and generalize, Workshop at ICLR, Apr 2021, Addis Ababa, Ethiopia. ⟨hal-03120669⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

29

Files downloads

100